Here are 7 things to keep in mind when cleaning your CRM:

  1. Data Enrichment: When your team starts to grow, you must start thinking about efficiency, and put an end to some tasks or automate them. To save your sales and marketing teams time by looking for information a quick solution is to incorporate simple enrichment tools. This will help populate important fields, while it does come with a price. The price is minimal in comparison to the time your sales reps or marketing team will save on manually finding that data.
  2. Be careful with data duplication process: Not all de-duping tools are customization to your organization. Sometimes you may want duplicates that could be merged if not identified during your data cleaning process.
  3. Normalized Data: Your data should be consistent for your reporting, lead distribution and segmentation. Make sure you have automatic workflow rules to normalize and standardize your data as soon as it is added to your CRM or Marketing Automation. Make sure to test this process prior to cleaning historical data.
  4. Align Marketing with Sales: in the best interest of your business or company, you had better do this fast. Sometimes the sales and marketing departments can be different and do things independently. You must define a process that is responsible for the collection of information between each.
  5. Data Quality Reporting: By creating a data quality scorecard or data quality reporting you will be able to identify when your CRM needs to be cleaned and what processes are needed to be changed.
  6. Training: No matter what data governance rules you have, system’s automation or processes in place without team’s willingness to keep data deduped and complete you will never achieve high data quality. Therefore, it is important to train your team in the importance of keeping data up to date, complete and clean.
  7. Outdated Data: Sometimes the cost of data clean-up is greater than the results. That is the case for outdated data and disengaged contacts or leads that should be excluded from the initiative in order to decrease the cost of data verification and appending.

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